Education
What Does Online Mentorship of
Secondary Science Students
Look Like?
CATRINA T. ADAMS AND CLAIRE A. HEMINGWAY
Mentorship by scientists can enrich learning opportunities for secondary science students, but how scientists perform these roles is poorly
documented. We examine a partnership in which plant scientists served as online mentors to teams conducting plant investigations. In our
content analysis of 170 conversations, the mentors employed an array of scaffolding techniques (encouraging; helping clarify goals, ideas, and
procedures; and supporting reflection), with social discourse centrally embedded and fundamental to the mentoring relationship. The interplay of
techniques illustrates that scientist mentors harmonize multiple dimensions of learning and model the integration of science content and practice.
The mentors fulfilled self-identified motivation to promote their students’ interest and to enculturate students to the science community through
online discourse. The patterns of this discourse varied with the mentors’ gender, career stage, and team–mentor engagement. These findings
address research gaps about the roles, functions, and conceptions of scientists as online mentors; they can be used to guide program facilitation
and new research directions.
Keywords: mentoring, science learning, online inquiry, discourse, student–teacher–scientist partnership
M
entorship plays a strategic role in efforts to recruit and retain successful science students in higher education settings (Ramirez 2012) and to engage youth in science
(NRC 2009). There is evidence for a wide range of benefits
to mentees (Eby et al. 2008). Between the rationale for implementing mentorship and its outcomes lies a vast expanse.
Many training materials outline mentoring best practices
(NAS 1997, Zachary 2000, Handelsman et al. 2005), but what
mentors actually do to support mentees is seldom studied.
One area of investigation in diverse educational settings
is how mentors and mentees conceptualize mentoring and
negotiate the mentoring relationship (Koballa et al. 2008,
Deaton and Deaton 2012, Straus et al. 2013). In academic
medicine and graduate education, the traditional apprenticeship model has persisted for generations. Learning at
the elbows of experts is now increasingly common also for
secondary science students through informal science learning and student–teacher–scientist partnerships. Mentors in
such a cognitive apprenticeship model (Collins et al. 1991)
guide novice learners through science-in-the-making experiences. Face-to-face programs remain common, but online
mentorship is gaining ground as a means to equalize access
to science experts and capitalize on anytime, anyplace learning collaborations. Where, when, and with whom mentoring
occurs has implications for how the relationships unfold.
Narrowing the focus to secondary science apprenticeship models, little is known about the nature of interactions
among students, teachers, and scientists as members of
learning communities. A particularly rich and promising
source of data to document these relationships is discourse.
Discourse makes thinking—attitudes, understandings, and
skills—visible. It also reveals facets of a scientific discipline’s
culture. In the case of scientist visits to high school classrooms toward the end of student investigations, Peker and
Dolan (2012) identified a type of division of labor in which
scientists offer students explanations of scientific phenomena or the nature of science, whereas teachers ensure that the
students can access this information, and both promote the
idea of scientific community. Discourse conducted online
rather than in person may influence the kinds of questions
that the students ask (Kubasko et al. 2008). Online discourse
with scientists is a relatively unexplored avenue in secondary
school settings.
It is also useful to consider how mentoring discourse
might vary with mentor characteristics. Gender and career
stage are reported to influence some aspects of how individuals engage in the scientific enterprise. Women faculty
members use more active teaching and learning approaches
than do men, and women frequently assume a facilitator or
delegator style, emphasizing their role as guide, consultant,
BioScience 64: 1042–1051. Published by Oxford University Press on behalf of the American Institute of Biological Sciences 2014. This work is written by US
Government employees and is in the public domain in the US.
doi:10.1093/biosci/biu147
Advance Access publication 17 September 2014
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Education
or resource to students rather than a role of transmitting
knowledge, setting goals, and providing feedback (Grasha
1994, Nelson Laird et al. 2007). Context, however, matters.
In one-on-one clinical or thesis advisor settings, all faculty
members preferentially use personal model, facilitator, and
delegator styles (Grasha 2002). Turning from gender trends
to career-stage effects on performance, a longitudinal journal study showed that the peer-review quality of an individual reviewer decreases over time (Callaham and McCulloch
2011). And when protégés have mentors near the end of
their careers, the protégés’ own mentorship performance
tends to be lower (Malmgren et al. 2010). We were interested
in whether the patterns that scientists exhibit in their working lives carry over to how they engage in outreach.
A shared view of teaching and mentoring frameworks
is that learning occurs on multiple dimensions. Moreover,
whether it is traditional or online, formal or informal, learning is an inherently social activity. Studies of online higher
education environments using the community of inquiry
framework characterize interactions across social, teaching,
and cognitive presences (Garrison 2007). To advance K–12
science education reforms, tying together the conceptual,
epistemological, and social dimensions for learners is advocated (Duschl 2008). In the mentoring literature, social and
cognitive or conceptual elements persist, and identity is
also prominent. In her early review of academic mentoring,
Jacobi (1991) noted that, although an operational definition
is elusive, three consensus functions emerge: (1) emotional
and psychological support, (2) career and professional
development assistance, (3) role modeling. Considering
out of school youth mentoring, Rhodes and DuBois (2008)
described relationships with adults as supporting socialemotional, cognitive, and identity development. These
frameworks offer a lens to contextualize mentor discourse.
We draw together mentoring, science learning, and
e-learning research to examine a collaboration in which
scientists mentor secondary school students as they design
and carry out plant investigations in their classrooms
(Hemingway et al. 2011). We aim to document how mentors
pose questions and provide feedback to science students by
describing the spectrum of techniques used across a robust
sample of volunteers. Three related questions shape the
study:
(1)How do mentors communicate online with secondary
school students to support science investigations?
(2)Do patterns of discourse vary by mentor characteristics
of gender and career stage?
(3)How do scientists perceive their role as mentors?
controlled experimentation or include observational studies;
their experience with inquiry often determines how guided
or open student projects will be. The research questions
and plants used vary widely; however, in all investigations
students are guided to collaboratively develop a research
question on a core idea in plant biology, plan and carry out
an investigation to answer the question, analyze the data, and
make sense of the findings.
Past and present team projects with their associated dialogs are available at www.plantingscience.org. The platform
is a customization of the open-source content management system Zikula. Asynchronous nonthreaded discussion
boards for each research team and their mentor capture the
students’ thinking and the scientists’ mentoring techniques.
A welcome message from the program coordinators starts
each dialog with an introduction of the assigned mentor
and encouragement to the mentor to begin a conversation
with his or her teams. Classroom teachers are encouraged to
have the students post to the mentors through all phases of
the project, from initial brainstorming to final presentation.
Context of the study
The PlantingScience online community is designed to foster
student learning of scientific practices and plant biology
through interactions with scientist mentors. Students work
in small teams on investigations extending from 3 to 12
weeks. Classroom teachers choose one of the available modules and influence whether the projects will be limited to
Data sources and sampling
We conducted a content analysis of scientist mentors’
conversations in the online learning community. Because
content analysis is a time-intensive method, we selected
two periods (2007–2008 and 2011–2012) that represent
the first and last years of funding under the acknowledged
grant. From a total of 3204 team–scientist pairs, we used a
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The mentors and the students
The mentors in the PlantingScience community range from
undergraduate students to professor emeriti and belong to
more than 14 scientific societies that partner in the program.
Mentor recruitment occurs through the partner organizations and an online registration form, which the program
staff then screens for fit. A special cohort of graduate students and postdoctoral researchers make a larger than usual
mentoring commitment. New volunteers receive initial
preparation via a mentoring guide that covers both logistics
and example prompts to help the students think about everyday experiences with plants, how scientists work, biology
content, and scientific evidence. The participants in an active
session are matched to teams on the basis of their age and
content-area preferences. The mentors receive just-in-time
tips through weekly newsletters. Following an online session,
participating volunteers receive surveys to complete about
their mentoring experience.
The students mentored by the volunteer scientists enter
the program through their teachers, who are typically seeking inquiry learning opportunities for their students. The
participating classrooms (60% high school, 40% middle
school) come from both rural and urban and both public
and private schools across the country. Here, we provide only
basic information about the students and teachers, because
their engagement is reported elsewhere (Peterson 2012,
Stuessy et al. 2012).
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stratified random sampling plan to generate a subset of 170
conversations. The sample represented projects characterized by low (1–3 mentor, 1–5 student comments), medium
(4–7 mentor, 6–10 student comments), and high (more than
8 mentor, more than 11 student comments) levels of interaction, which were based on the average number of mentor
and student comments recorded in the longitudinal tracking.
In this sample, 31% of the dialogs exhibited low, 36% showed
medium, and 33% had high levels of interaction.
The 170 conversations included 1086 messages posted by
the mentors, of whom 105 were female and 65 male. We classified the mentors by career stage, and the random sample
included 87 early-, 76 mid-, and 4 late-career scientists
(3 lacked the relevant profile details). Therefore, most of the
sampled mentors were female (62%) and in early (51%) or
mid- (45%) career stages. In order to facilitate a comparison
of demographic patterns between the groups which were
unevenly represented, the raw counts were normalized. The
demographic classes were assigned a weight (e.g., female, 1;
male, 1.62), which was used to adjust the raw counts before
determining a weighted percentage.
Surveys completed by the volunteer mentors served as a
data source for personal reflections about their roles in the
scientist–student partnership. Basic motivations underlie the
mentors’ views of their purpose for volunteering and, therefore, shape their self-perceptions of their role as mentors.
The end-of-session reflection questions have varied over the
years, so we selected surveys from spring 2012 and 2013 that
included an identical direct, open-ended question.
Data analysis
Dialog transcripts from the archived platform database were
imported into Dedoose (www.dedoose.com) for coding and
analysis. As the unit of analysis, we used each time-stamped
message posted by a mentor. Like that of other researchers
(Aviv et al. 2003, Gorsky et al. 2012), our purpose in using
the message as the unit of analysis was to gain a perspective
of the underlying structure in the asynchronous collaboration. The messages ranged from a sentence to several paragraphs in length. We applied multiple codes to a message if
the various sentences served different purposes.
We used the constant comparison method developed
from grounded theory (Strauss and Corbin 2008) for qualitative data analysis. Therefore, as we initially reviewed the
posted messages, we did not attempt to enforce terminology
and codes from prior studies. Instead, we began with open
coding to identify the full spectrum of distinct types of mentor acts and only later clustered the actions around emergent
themes. The initial set of codes was revised through an
iterative process of independently coding the same excerpts,
comparing the codes for a message within and between
mentors, and discussing the discrepancies. In the next coding phase, we used patterns in content clustering by the type
of act to reduce and group the categories. This yielded 26
distinct categories of action used by the mentors, which we
then organized on the basis of the general function that the
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cluster of related actions served (see figure 1 and the supplemental material for example dialogs). Once core themes
emerged in our data, we considered the overlap of the top
hierarchical categories with theoretical frameworks, which
was generally high, except for our use of a procedural category. We tested our code application agreement using the
Dedoose training module; interrater reliability between the
two authors was very good (pooled Cohen’s kappa statistic,
κ = .82).
Goodness-of-fit tests were used to compare the frequency
distributions with respect to the mentors’ gender, career
stage, and interaction level for the function of the dialog and
features of the mentor posting. We used a Mann–Whitney
test (a nonparametric one-way analysis of variance) to
examine differences in the number of posts with respect to
gender and a Kruskal–Wallis test (an extension for more
than 2 groups) to examine differences according to career
stage.
For preliminary descriptive purposes, the survey responses
were categorized by theme to provide a sense of the scientists’
motivations for working with secondary school students.
How do scientists engage online to support student
team investigations?
Our guiding questions were the following:
• How do mentors communicate online with secondary
school students to support science investigations?
• Do patterns of discourse vary according to the mentors’
characteristics?
• How does this relate to how the scientists perceive their
roles as online mentors?
We first document broad patterns of the scientists’ asynchronous discourse with the students throughout the team
research projects. We explore the influence of engagement
levels on the dialog patterns. Then we examine whether
mentor characteristics such as gender and career stage
influenced the mentors’ techniques and style. And finally, we
draw on the survey results for the scientists’ motivations and
views of mentoring.
The scientists communicated online in ways that could be
grouped as serving four general functions. Over half of the
mentor acts (55%) served social functions. We defined these
as indicating the mentors’ role to broker expectations, build
and maintain relationships with team members, and acculturate the students to science. The second most common
mentor act (26%) was procedural—that is, comments and
questions to help the students clarify how their plans and
procedures related to the goals of the investigation, choose
and understand procedures, and solve procedural problems.
Conceptual comments and questions accounted for 17% of
the acts; we defined these as helping the students clarify their
ideas about the content area or access resources to increase
their own conceptual understandings. Epistemological acts,
those that aided the students in reflecting on their own
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Figure 1. The frequency of particular types of actions that mentors used within four broad categories that serve social (open
bars), procedural (hatched bars), conceptual (black bars), and higher-level understanding (solid gray bars) functions.
understandings and higher-level conceptual thinking, were
relatively infrequent (2%).
Particular techniques within each functional grouping
stood out (figure 1). The mentors most frequently offered
affirmations to the student teams (n = 764) and sought to
set expectations with them about the student–scientist mentoring relationship (n = 623). Comments about how science
works and generalities about career pathways (n = 126) were
more common than personal accounts about their own life
as a scientist (n = 46). The mentors primarily asked questions to elicit student ideas about procedures (n = 387) and
secondarily offered direct instruction on how to perform a
technique or other particular element of a research protocol (n = 272). In roughly equal proportions, mentors asked
probing questions about student conceptual ideas (n = 271)
and provided information or resources for the students to
further explore the content (n = 255). To prompt student
understanding, the mentors most frequently encouraged the
students to think about real-world connections and applications of the teams’ research (n = 46).
There was a complex interplay in how the acts cooccurred. The mentors regularly paired comments that
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affirmed and set expectations in discourse with the student
teams and used these in conjunction with questions about
the students’ ideas about procedures or concepts (figure 2).
Affirmations co-occurred with nine other acts, expectations with six other acts. The mentors often asked about the
students’ ideas about procedures and concepts together in
one post—for example, Your idea of testing whether plants
can survive in an airtight box sounds very interesting . . . .
How would you set up your experiment to test this idea? Do
you think seeds need air to germinate and survive as young
plants?
Do patterns vary with relative engagement between student team and
scientist mentor?
The number of posts exchanged between the student teams
and their mentors ranged widely. Why some conversations
were brief and others really took off likely relates to multiple underlying factors (e.g., computer access, motivation
levels) that we do not attempt to disentangle here. Rather,
we next investigated the influence of engagement level
on how the mentoring played out. The dialog patterns differed significantly according to the interaction levels of the
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patterns were least pronounced for social
discourse.
To drill further into the discourse patterns, we considered gender and careerstage patterns in the average number of
posts and in the structure of the posts,
taking into account their complexity (i.e.,
the number of codes applied per post)
and length (i.e., the number of characters per post). The average number of
posts was not significantly different for
the male and female scientists (z = .62,
not s­ignificant [ns]), although it was
slightly higher for the men (6.68 and
6.35, respectively). Although the average number of posts was lowest for
the mid-career scientists and highest for
the late-career scientists (early career,
mean [M] = 6.87; mid-career, M = 6.15;
late career, M = 8.5), the differences
across career stages were not significant
(H(2) = 1.16, ns). The posts made by the
male and female mentors were similar
in complexity (χ2(2) = 5.22, ns) but
differed significantly in length (χ2(2) =
8.92, p = .01). The male mentors contributed 76.4% of the longest posts. The
career-stage patterns in the structure of
Figure 2. Mentor acts with the highest frequency of co-occurrence. The number
the posts differed significantly in comof co-occurrence instances are indicated, and the width of the connecting
plexity (χ2(4) = 31.78, p < .0001) and
lines is scaled with frequency. Acts with social function are outlined in white,
length (χ2(4) = 60.51, p < .0001). The
those with procedural function are outlined with dashed lines, and those with
early-career mentors contributed 50%
conceptual functions are outlined in solid black.
of the most complex posts, whereas the
late-career mentors contributed 45% of
the least complex posts. The late-career mentors contributed
team–mentor relationships (χ2(6) = 24.39, p = .0004). When
91% of the longest posts.
the interactions were low, the mentor discourse remained
primarily social, but when both the team and the mentor
How do scientists perceive their roles as mentors?
were highly engaged, the mentors focused more on proceThe survey responses (n = 152; table 1) indicated that the
dural and higher-level understandings (figure 3).
volunteer mentors were motivated primarily to inspire
young students’ interest in biology or botany (41%) and to
Do patterns vary with mentor characteristics?
improve students’ understanding of science and scientists
The function of the acts and structure of the posts show
(22%). These motivations seem tied to mentoring styles
demographic patterns (figure 4). The female and male
emphasizing social connection, encouragement, and socialmentors differed in the frequency of their acts with social,
izing into science. Fewer of the scientists reported a desire
conceptual, procedural, and epistemological functions
­
to share knowledge or to assist with projects (14%), which
(χ2(3) = 9.91, p = .02). The gender patterns were least
align with conceptual and procedural comments. Beyond
pronounced in the social function and most pronounced
motivations that can be linked to the self-perception of
in epistemological discourse, with the men far more frementoring roles, the volunteers cited four other intentions
quently making comments to help the students reflect on
related to involvement and communication with the comtheir understanding. There were also significant differences
munity at large.
according to the mentor’s career stage (χ2(6) = 66.03,
p = .0001). Compared with the mentors in mid-career, the
Finding 1: Social discourse is integral
mentors at early- and late-career stages focused more on
Social discourse was an integral element of the scientists’
higher-level understanding issues. Late-career mentors also
techniques in online science mentoring. Our results in this
focused more on conceptual discourse. The career-stage
online learning community reinforce aspects of previous
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Figure 3. The distribution of mentor acts by varying levels of engagement. The data are shown as the percentage of the total
number of mentor acts that were social, procedural, conceptual, or epistemic in function for mentor–student team dialogs
categorized as low (1–3 mentor and 1–5 student comments), medium (4–7 mentor and 6–10 student comments), and high
(more than 8 mentor and more than 11 student comments) levels of interaction. The dialog patterns were significantly
different, depending on level of engagement (chi square tests, p = .0004).
findings in a classroom setting (Peker and Dolan 2012) and
illustrate ways that scientists inherently harmonize multiple dimensions of learning (sensu Duschl 2008) through
their discourse with science learners. Moreover, the high
co-occurrence of the mentors asking about the students’
ideas about the underlying biology with questions about the
teams’ experimental design demonstrates that the scientists
modeled for students the integration of science content and
practices. These findings provide insights into the authenticity of science learning in online environments.
Although the contexts differ remarkably, our results of
mentor discourse showed a frequency of social comments
slightly lower than those reported in online higher education. Within distance learning research, there is interest in
the mitigating role of social presence to offset a potentially
impersonal nature of virtual learning and the essential place
of teacher presence (Russo and Benson 2005, Garrison
2007, Sheridan and Kelly 2010). A pattern of 60% social,
20% teaching, and 20% cognitive presence recurs in online
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college courses, which Gorsky and colleagues (2012) took to
indicate that deep learning is not happening in nonmandatory asynchronous forums.
In our study, we attributed the high proportion of social
comments to four types of behaviors common among the
mentors: (1) coping with the distance inherent in asynchronous online communication with students they have not
previously met, (2) negotiating and maintaining a personal
relationship, (3) actively socializing and welcoming novice
learners into the science community, and (4) fulfilling their
self-identified motivation to inspire interest in science and to
help students see what scientists are like. We interpreted the
pattern of the scientists’ use of social comments, regardless
of gender or career stage, as their seeking to establish a positive connection with the students at the outset. In a mentoring relationship, the foundation for the learning relationship
is built in the preparation and negotiating phases (Zachary
2000). It is unlikely that our program training influenced the
mentors to emphasize social over other discourse elements,
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Figure 4. The function of mentor acts and the structure of posts as a function of mentor gender and career stage. The data
are shown as normalized percentages. The distribution of dialogs across the four functional categories of mentor acts
differed significantly between the male and the female mentors (chi square tests, p = .0004) and among career stages
(p = .0001). Although neither gender frequently used discourse in the understanding category, the male mentors
contributed 59.7% of all comments related to student higher-level understanding. The length of posts differed significantly
as a function of gender (p = .01), whereas differences in complexity as a function of gender were not significant (p ≥ .05).
The male mentors contributed 76.4% of the longest posts. The career stage differences were least pronounced in social
function, with male mentors contributing 52.1% of the social comments. Both the length (p < .001) and the complexity
(p < .001) of posts differed significantly according to career stage. The late-career scientists contributed 91% of the longest
posts and 45% of the simplest, whereas the early career scientists contributed 50% of the most-complex posts.
because we cover many aspects of mentoring student investigations. Moreover, on the basis of comparisons of mentor–team pairs characterized by various interaction levels,
we suggest that the relationships require a sufficient baseline
of social development for discourse to move to higher-level
integration and analysis. We show that when mentor–team
engagement is high, the dialog is richer and deeper. Our
social function frequency might be inflated, because we
grouped comments about career pathways, the scientific
enterprise, and personal accounts of careers and scientific
life in the social category. Reclassifying the posts on these
topics separately as the nature of science would lower the
frequency of social discourse to 50.6%.
The relatively high proportion of questions and feedback
about research procedures reported here is not surprising,
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given the mentoring program’s aim to support openended plant investigations. Many middle and even high
school student participants reported a lack of experience
designing experiments to test research questions that they
had generated. The challenges that students experience
in manipulating multiple variables, understanding the
significance of controls, and deciding on the appropriate data to collect are documented for science learners
in both precollege and introductory college levels (NCES
2012, Brownell et al. 2014). Although it is not an overt
motivation for mentoring, the mentors’ focus on procedural knowledge and skills may relate to an underlying
view that, as scientists, they can best help students master
the conventions of what makes a good experiment and to
develop the problem-solving and critical-thinking skills
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Table 1. Responses to the survey question Why do you volunteer as a PlantingScience mentor?
Number of
responses
Percentage of
responses
“The idea of PlantingScience is a fulfillment of
my dream of an informal approach to motivating
youths in science and scientific studies—catching
them young.”
62
41
For the opportunity to work with a younger age
group
“It’s a pleasure to work as mentor for school
students. Their cute questions always impress me.”
48
32
Because mentoring or outreach is important
“As a student, I’ve been a beneficiary of a
substantial amount of mentorship. PlantingScience
provides a wonderful, novel, and unique way for
someone like me to mentor new generations of
students.”
35
23
To help students understand the culture of science,
think like scientists, or understand what scientists
do or are like
“I want to help students learn how to think for
themselves and question things around them.”
34
22
To share knowledge or help with projects
“To help children understand scientific concepts in
a fun, interactive environment.”
21
14
To become a better science communicator
“It’s a good chance to practice communication
skills with students.”
11
7
To connect with, understand, or help K–12
educators
“Being a mentor afforded me the ability to help
get in the trenches of science education where it
matters most—elementary and high schools.”
11
7
Category of motivation
Example mentor quote
To inspire young students’ interest in science or
botany
that are called on to plan and revise tests of scientific ideas.
An independent examination across the inquiry cycle
showed that PlantingScience mentors concentrated their
efforts on experimental design and procedures (Peterson
2012). Moving inquiry through to connecting and applying
ideas is a common difficulty in online higher education
(Garrison 2007). Although a more even distribution of
discourse across all phases of an inquiry is desirable, this
focus on research skills is interesting in light of recent studies showing a direct relationship between undergraduate
research skills and students’ self-confidence and research
career interest (Adedokun et al. 2013). The relatively high
proportion of procedural comments and the low proportion of higher-level cognitive comments in our study are
also influenced by two complexities arising from the ways
that secondary school teachers orchestrate classroom and
online activities. Although the program strongly encourages mentor involvement throughout the student projects,
some teams decide on a research question in class before
communicating with their online mentors. At the other
end of the inquiry cycle, it is not uncommon for students
to complete their final project presentations in class only.
In both the surveys and the focus groups, the participating
teachers reported that accessing computers for their classrooms has been one of the largest challenges they face. By
shifting the dialog related to question formation or final
presentation from online to classroom settings, teachers
may inadvertently confound communication during valuable opportunities for higher-level cognitive discourse.
Finding 2: Science enculturation is valued
The enculturation of students to the science community was
valued. Our data documenting what the mentors actually
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said to the students online and their self-reported motivations for mentoring reinforce that the volunteers in this program conceived an important part of their role as welcoming
the students into the science community. Socializing new
community members is a common theme in the professional development literature for K–12 teachers and higher
education faculty (Gehrke and Kay 1984, Cawyer et al. 2002).
Making meaningful personal connections and learning
social norms in the scientific community are important for
science learners at all stages.
Studying the socialization of graduate students, Weiss
(1981) found that frequent informal interactions with faculty
members significantly correlated with students’ professional
self-conception and commitment to their professional role.
Secondary students involved in scientist partnerships have
a strong interest in scientists as people, a finding in keeping with student learning being embedded in the context of
their lives and scientists serving to bridge cultural divides
(France and Bay 2010). In one study, high school students
overwhelmingly reported the best questions that they asked
of the scientists to be personal ones (e.g., How old were they
when they knew what they wanted to be?) and only rarely
identified questions about the nature of science as their
best questions (France and Bay 2010). Comparing high
school students’ communication modes with scientists about
nanotechnology, Kubasko and colleagues (2008) found that
students asked more personal questions via videoconference
and more about inquiry and interpretation through email.
Although the communication medium may influence content at any stage, precollege students have fewer opportunities
to interact with scientists than do undergraduate or graduate
students. Putting a personal face on students’ abstractions of
a scientist appears especially valuable to secondary students.
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Finding 3: Demographics affect discourse
There were gender and career-stage differences in the scientists’ online discourse with the science learners. Our results
on career-stage patterns have implications for the formation and facilitation of mentoring programs. These findings
present justification for selectively recruiting scientists at
either end of their careers (graduate students and scientists
nearing or in retirement). The multiple demands to excel in
research, teaching, and service faced by mid-career scientists likely reduce their time and attention. The late-career
scientists demonstrated their experience by guiding novice
learners as online mentors, but they did not volunteer in
large numbers. In contrast, the graduate students appeared
particularly interested in science outreach opportunities. In
the business e-mentoring program studied by Panopoulos
and Sarri (2013), individuals younger than 46 were more
eager to volunteer. An advantage of recruiting graduate
students is the potential long-term impact of their involvement in K–12 education and on their own research careers.
Diverse benefits to STEM (science, technology, engineering, and math) graduate students, K–12 teachers, and
students alike have previously been demonstrated (AAAS
2013). The structural differences that we observed between
the early- and late-career scientists’ posts are intriguing.
Late-career scientists with more teaching experience may
have developed strategies to cover fewer topics in greater
length.
The gender patterns in our results are harder to square
with previous theoretical and practical perspectives of mentoring. In contrast to our findings, Panopoulos and Sarri
(2013) found gender differences in e-mentoring to relate
more to its adoption than to its use. Mentoring online did
not pose a barrier to the female scientists that we studied;
women outnumbered male scientists in both the stratified
random sample (62%) and the program overall (60%). The
gender difference in post length diverges from prior research
on peer mentors that showed that men made shorter statements in online communication than did women (SmithJentsch et al. 2008). We also found that male mentors focused
more on higher-level understanding. It is difficult, at this
stage, to identify the underlying influences on gender differences in discourse patterns and their implications for
mentoring programs.
Our study of the spectrum of mentoring techniques used
across 170 mentor conversations was effective in identifying
online discourse patterns. New questions arise that are best
addressed in a fine-grain analysis in which student response
to mentor posts are considered within targeted discourse
areas. Without an analysis of student and mentor posts, we
cannot determine whether a mentor style of longer, morecomplex posts with more focus on concepts, procedures, and
higher-level understandings are more effective for student
learning or student motivation than are shorter, simpler
posts with more focus on social acts. It is quite possible that
the most effective style will vary, depending on a student
team’s background and learning style. Further study of the
1050 BioScience • November 2014 / Vol. 64 No. 11
sequence and progression of exchanges is warranted to
discern gender and individualistic mentoring styles and to
determine where in the conversation flow mentors introduce
higher-level understanding.
Conclusions
Our content analysis of mentor discourse showed that scientist mentors inherently model the integration of multiple
dimensions of learning for students in the online community.
The mentors facilitated the students’ engagement in their
own learning by asking the teams to articulate their thinking
about biology concepts and investigation procedures, embedding these prompts in a social fabric of encouragement and
expectations. The mentors supported the students’ identities
as researchers by pulling back the curtain to reveal how the
scientific enterprise works. The scientists who volunteered to
mentor fulfilled self-identified motivations to inspire interest
in science and to help students see what scientists are like,
and they used conversation techniques to actively welcome
novice learners into the online science community. On the
basis of our findings on the differences in discourse patterns
related to engagement level, mentor gender, and mentor
career stage, we encourage science mentoring programs and
the volunteers in them to closely examine the discourse to
better understand these complex interactions and their influence on student science learning.
The mentorship of science learners holds great promise
as a model to enhance students’ understanding of, skill in,
and interest in pursing science. However, little rigorous
research is available on student–teacher–scientist partnerships (Sadler et al. 2010). Studies quantifying learning
gains, analyzing programmatic elements, and examining
participant interactions are all needed to understand what
and how programs are successful. Our results provide
much-needed data on the roles, functions, and conceptions
of scientists serving as online mentors to precollege science
students and new perspectives on the demographic characteristics of mentor performance that have implications for
forming and facilitating mentoring communities.
Acknowledgments
We are indebted to the scientists, teachers, and students who
make PlantingScience possible. We thank steering committee members and collaborators Jane Larson at Biological
Sciences Curriculum Study and Carol Stuessy and her excellent team of graduate student researchers at Texas A&M
University for conversations and perspectives that have
enriched the work. We appreciate the helpful comments of
three anonymous reviewers. We also thank the 14 scientific societies, including AIBS (http://tinyurl.com/khcspkz),
who are partners of the program. This material is based on
work supported by the National Science Foundation (NSF
grant no. DRL-0733280) and the article written while the
second author serves at the NSF. Any opinion, findings, and
conclusions or recommendations expressed are those of the
authors and do not necessarily reflect the views of the NSF.
http://bioscience.oxfordjournals.org
Education
Supplemental material
The supplemental material is available online at http://
bioscience.oxfordjournals.org/lookup/suppl/doi:10.1093/biosci/
biu147/-/DC1.
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Catrina Adams ([email protected]) is Education Director for the Botanical
Society of America in St. Louis, Missouri and coordinates the PlantingScience
program. Claire Hemingway is currently Science Advisor in the Division of
Environmental Biology at the National Science Foundation in Arlington,
Virginia and previously led the PlantingScience program.
November 2014 / Vol. 64 No. 11 • BioScience 1051